International Forum of Educational Technology & Society, National Taiwan Normal

University, Taiwan

Data Theatre as an Entry Point to Data Literacy

Author(s): Rahul Bhargava, Amanda Brea, Victoria Palacin, Laura Perovich and Jesse Hinson

Source:

Educational Technology & Society

, October 2022, Vol. 25, No. 4 (October 2022), pp.

93-108

Published by: International Forum of Educational Technology & Society, National Taiwan

Normal University, Taiwan

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Bhargava, R., Brea, A., Palacin, V., Perovich, L., & Hinson, J. (2022). Data Theatre as an Entry Point to Data Literacy.

Educational Technology & Society, 25 (4), 93-108.

ISSN 1436-4522 (online) and 1176-3647 (print). This article of the journal of Educational Technology & Society is available under Creative Commons CC-BY-NC-ND

3.0 license (https://creativecommons.org/licenses/by-nc-nd/3.0/). For further queries, please contact Journal Editors at ets.editors@gmail.com.

Data Theatre as an Entry Point to Data Literacy

Rahul Bhargava

, Amanda Brea

, Victoria Palacin

, Laura Perovich

and Jesse Hinson

Northeastern University, USA //

Univerisity of Helsinki, Finland // r.bhargava@northeastern.edu //

brea.am@northeastern.edu // victoria.palacin@helsinki.fi // l.perovich@northeastern.edu //

j.hinson@northeastern.edu

Corresponding author

ABSTRACT: Data literacy is a growing area of focus across multiple disciplines in higher education. The

dominant forms of introduction focus on computational toolchains and statistical ways of knowing. As data

driven decision-making becomes more central to democratic processes, a larger group of learners must be

engaged in order to ensure they have a seat at the table in civic settings. This requires a rethinking to support

many paths into data literacy. In this paper we introduce “data theatre,” a set of activities designed for data

novices that may have limited experience or comfort with spreadsheets, math, and other quantitative operations.

Through iterative co-design over three workshops, we tested and produced two activity guides for educators,

building on long-standing practices in participatory theatre that center social justice and liberation. Our initial

findings provide very early evidence that this approach can help these learners overcome hesitations to working

with information, begin building a critical perspective when viewing data, and create emotionally impactful data

stories told through theatrical performance. This prototype work suggests to us that the concept of “Data theatre”

warrants further study to build a more robust understanding of its affordances and limitations.

Keywords: Data literacy, Participatory theatre, Education, Social justice

1. Introduction

Data is shaping our lives and culture in a growing number of ways, from enabling our everyday digital activities,

to supporting scientific breakthroughs, to guiding decision-making at local and international scales. Yet

significant work has documented the negative impacts of this trend towards datafication (Datafication is a

technological trend that seeks to capture large proportions of human activity and turn them into digital and

quantitative data for processing (Couldry & Mejias 2019)), such as racialized outcomes in facial-detection based

policing, embedded gender bias in automated hiring, racial disparities in algorithm-based healthcare diagnosis,

and more (Eubanks, 2018; Noble, 2018; O’Neil, 2016). In response to these examples, broader conversations

have begun to integrate questions of justice and equity in data-centered projects (D’Ignazio & Klein, 2020;

Raffaghelli, 2020; Williams, 2020).

In parallel, data literacy has grown as a major topic in educational settings across technical and socio-political

domains (Prado & Marzal, 2013; Klenke et al., 2020; Ryan et al., 2019; Timmermann & Havemann, 2020). We

utilize a four-part definition for “data literacy,” engaging components related to acquiring data, processing and

analyzing data, representing data, and storytelling with data for some purpose (Bhargava & D’Ignazio, 2015).

Regions in Rwanda (UNICEF Rwanda, 2016) and Australia (“Data Literacy,” n.d.) have already introduced data

literacy as part of their digital literacy curriculums, and the European Union has classified data literacy as an

essential skill for the future and since 2016 has implemented policies and initiatives to support its development

(Carretero et al., 2017). Typical introductions to data literacy at the university level follow patterns that have

long existed, privileging those with math and technology fluency and thus reinforcing existing power

asymmetries in data via their structure and application (Bourdieu & Passeron, 1990). These parallel trends

expose a growing need for educational data literacy practices that center principles of justice, emancipation, and

participation.

Motivated by this need, in this paper we build on historical theories of education and embodiment and

participatory theatre to introduce our collaboratively designed “data theatre” workshop. The first workshop

activity, entitled “Embody a Dataset,” invites participants to choreograph a representation of data using the other

participants’ bodies. The second, entitled “Make a (Data) Scene,” asks teams of participants to find a story in a

dataset and quickly design and perform a short scene to tell that story. We introduce motivations, summarize

inspirations and related work, review the design process, document the activities, and discuss initial findings.

This offers an initial case study of introducing data literacy centered around concepts of social justice and

complements our larger body of work building arts-based invitations for data literacy learners.

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1.1. The need for new paths into data literacy

Justice-informed pieces of building data literacy can look radically different than dominant current approaches.

An important question to engage is precisely what “justice” means in our work. At a high level we embrace

terms like “justice,” “equity,” and “capacity” to align with traditional struggles to retake power by those without.

We take “social justice” to be a phrase defining a system where members of society can participate fully in their

roles, achieve social mobility, and access collective support structures provided by their communities. We

respect and engage the phrase’s long history in Western religious thinking, and its connections to more modern

movements such as Liberation Theology (Gutierrez, 1988), but do not choose to center those roots in our

application.

Taking approaches built on this definition is becoming critical as more decision-making in civic settings has

become data-driven. Those who do not “speak” the language of data do not have social agency in this setting; we

define that as an emerging problem for democratic society. How can those without a seat at the table in civic

decision-making contexts that center data be authentically invited to participate? What barriers are holding them

back? This work is targeted at developing alternate paths into this set of skills to create new ways for all people

to learn to “speak data” so they can engage in society critically. We are informed by a conception of three main

barriers: (1) focus on statistics (2) centering of technologies (3) control over impact.

First, we find that statistical analytic processes are the primary goal of many introductions to data. Browsing a

mix of educational and professional training, one finds statistics and computation are most often front and center,

introduced without significant discussion of context and ethics (Oliver & McNeil, 2021). This pattern follows a

long arc of abstract mathematical thinking being centered in American educational curricular guidelines

(Scheaffer & Jacobbe, 2014). The current manifestation of this history is the “STEM” approach to curricular

priorities, proposed as a response to forecasted economic workforce needs (National Academies, 2007). There is

a history of robust critique of the form and intent of US math education (Harouni, 2015). This has led to many

learners forming identities as “math” or “non-math” people; definitions only somewhat related to their actual

competence (Leonard et al., 2020; Cribbs et al., 2015). As data science was introduced and claimed by the field

of statistics (Gould, 2017), quantitative analytical approaches became the standard at the expense of creating a

more inclusive set of pathways into the field of working with data. Any introduction to data framed through a

mathematics lens immediately raises a massive barrier to the group identifying as “non-math” people.

Second, introductions to data literacy in learning settings typically rely on a techno-centric understanding of

these skills, focused on quantitative data stored and analyzed via computational means. Digitization has radically

altered the costs of collecting, storing, and retrieving data of any type, placing computation centrally within any

data project. Related Big Data skills have been in high demand for more than a decade (Manyika et al., 2011).

Focusing on software that supports algorithmic operations on large datasets privileges those with access to

computational tools, which differs across social, economic, and racial divides (Jackson et al., 2008). This

privileges those with power and access. In addition, Small Data continues to play a large role in social contexts

(Blair et al., 2014), despite the hype behind it’s “Big”-ger cousin (boyd & Crawford, 2012)

Third, datasets are too often separate from their potential applications and context of influence. Our prior work

has critiqued data projects for their persistent lack of transparency and reliance on extractive collected data

(D’Ignazio & Bhargava, 2015). The phrase “data is the new oil” is quite emblematic of the promise and peril of

this approach (Flender, 2019). There is a relevant parallel here - just as with oil, the benefits of data projects are

gleaned by those in power while the perils are experienced by those typically marginalized. This can be

attributed, in part, to the fact that most data are extracted from a population or setting of study and taken to

another context to be analyzed and interrogated. The analysis and impact conversations rarely integrate the

subjects represented in the data, nor the communities that data-driven decisions will impact.

Prior work has begun to address this. Qualitative data can be introduced alongside quantitative to introduce non-

computational analytical approaches (Henderson & Corry, 2020). Introductions can include concrete examples of

alternate modes of data analysis such as collaborative meaning-making in urban planning processes (Boston

Transportation Department, 2017), or story-finding and storytelling in the design of clothing and traditional

fabrics (Katuli, 2019; Perovich et al., 2020). These examples show how discussion, aesthetics, design, and object

construction (Willett et al., 2017) are forms of data analysis and storytelling. In addition, they demonstrate

community ownership of data analysis, addressing our third main critique.

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1.2. An arts-based data-theatre approach: Research questions

In this paper, we explore theatre, an arts-method, as a promising approach to address the broader need for new

paths into speaking the language of data. Art-based methods have long been used to enhance learning in diverse

disciplines, such as medicine (de la Croix et al., 2011), math (Hally & Sinha, 2018), and physics (Solomon et al.,

2021). These methods offer learners an opportunity to tap into their kinesthetic intelligence by using their body

to express or understand ideas, concepts, and experiences (Lowenfield, 1957). This may appeal to learners that

embraced performance-based media for sharing information, including data-based facts (such as Tiktok and

Instagram).

We describe the iterative design process, and initial findings, from a series of “data theatre” workshops we led

online and in person. Our design process finds ample overlaps between the world of participatory theatre, which

has a traditional focus on social justice, and our arts-based approaches to data literacy. The intersection between

data literacy, justice, and the barriers enumerated above, leads us to our driving motivation: exploring how

participatory theatre can help us build novel introductions to data that center on justice. Specifically, we

formulated three central research questions:

• RQ1 - Literacy: In what ways can theatrical activities introduce learners to data literacy skills?

• RQ2 - Critique: How might embodying data help lead to a critical questioning of datasets and their use?

• RQ3 - Impact: Does the process of performing a data story change performer or audience perspectives on

the subjects of the data?

We engage these questions in our workshop design and throughout this paper. At the same time, this early-stage

work is still speculative and exploratory as we work with learners to build a practice of data theatre, developing a

novel approach as a basis for further study. Future work will explore ways to more robustly assess causal

relationships between workshops and data literacy outcomes, relative to more traditional data learning activities.

2. Related work

In broaching the questions of what “data theatre” might look like, what justice-oriented learning goals it might

fulfill, and its potential for impact, we build on significant work in three areas: (1) education and embodiment,

(2) participatory theatre practices (3) professional theatre practice.

This is guided by central design principles established in our previous work (Bhargava & D’Ignazio, 2015):

• Participation from all parties

• Learner-guided explorations

• Facilitation over teaching

• Accessibility to a diverse set of learners

• Focus on real problems in the community

In prior projects these principles have guided the development of learning experiences to design and paint

community data murals (Bhargava et al., 2016) and build physical data sculptures with crafts materials

(Bhargava & D’Ignazio, 2017). This new foray into activities for data theatre introduces our work to a rich

history of education through embodiment, and introduces a new performative output for the learning activities

we create.

2.1. Education and embodiment

Research in education and embodiment surfaces the importance of emancipation through critical data literacies

and points to the ways in which physical movement and action may alter understanding, perception, and

memory.

Many pedagogical approaches have embraced justice and equity at their core. We take strong inspiration from

Dewey’s foundational connection between education and democracy (Dewey, 1903) and Freire’s approach to

emancipatory education (Freire, 1968). In the context of data literacy, we specifically posit that the application of

this idea to modern day involves both understanding the oppression of data processes integrated into civic

governance and taking back power to use data in service of collective justice (Couldry & Mejias 2019).

Functionally, this builds on research into how learning can be more effective when the subject is interrogating

issues that matter in their communities (Zeidler et al., 2005). From this work we draw an approach that works

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with local community data, provides creative freedom to find narratives in it, and focuses on potential real-world

impact of the process.

An emerging definition of “critical data literacy” applies Freire’s approach to the concept of data literacy (Tygel

& Kirsch, 2015; Sander, 2020). This conceptualization offers links to previous movements in critical media

literacy and digital literacy (Knaus, 2020). It also provides a theoretical framework to underpin the design of

activities and interventions for educational contexts that seek to raise awareness of the datafied society around us

and put data in the hands of the communities it is too often extracted from. Critical data literacy embeds a hope

for empowerment through activism and social change (Gutiérrez, 2018). The approach of critical data literacy

offers us theoretical framing within which to situate our activities, and a set of related projects that our work

connects to.

Finally, we find inspiration in embodied approaches to learning. These build on understandings of learning as

situated in social context, activity, norms, experience, and physicality (Lakoff & Johnson, 1999). Indeed,

scholars have acknowledged the power of embodied experiences to shape learning (Höök, 2018) and promote

empathy (Yap, 2016). Going further, we embrace the idea that all forms of knowing are rooted in the “somatic

phenomenon” of the learner - the physical experiences they undergo and create while in learning settings

(Abrahamson & Lindgren, 2014; Malinverni & Pares, 2014). Data science educators have built on this type of

definition to offer activities such as bodily data sorting and physical infographics (Sommer & Polman, 2017).

The latter two are examples of participatory simulations - opportunities to socially construct an understanding of

phenomenon by embodying it. In this practice the act of “knowing” itself is participatory and shapes outcomes

(Barab et al., 1999). The emerging concept of “data feminism” embraces this, suggesting data embodiment and

data “visceralization” be valued as ways of knowing data (D’Ignazio & Klein, 2020).

Furthermore, our definition of justice comes together with a motivation in an attempt to embrace

“epistemological pluralism” in the domain of data science. As defined by Turkle and Papert, epistemological

pluralism is an “acceptance of multiple ways of knowing and thinking,” with each “equally valid on its own

terms” (Turkle & Papert, 2015). Their conceptualization connects to gender rights in learning settings, respect

for previously dismissed concretized learning, and notions of body syntonicity. We are inspired and motivated to

create a data science that engages and values epistemological pluralism, moving beyond a belief that algorithmic

and formal thinking is naturally superior. Embodied approaches to building data literacy are one piece of this

puzzle.

2.2. Participatory theatre

Our work draws on the long-standing focus on liberation in participatory theatre and the many practical activities

built by expert practitioners in this space over decades of contextual work with communities.

The approach of “epic theatre” is a common root for many of these practices, built around the idea that theatrical

practices can provoke the audience to reflect on the world as it is, rather than suspending disbelief and entering

an imagined world via the performance (Styan, 1981). Theatre of the Oppressed, a form of theatre designed for

social and political activism, explores oppression in all forms - internal / external, individual / societal, and more

(Boal, 1993). Verbatim theatre (Catchesides, 2020), and image theatre (Farmer, 2014) are all examples of other

approaches that have grown from this common theoretical core and been successfully used for empowerment in

communities. Playback theatre, where participants share experiences which are then “played back” to the broader

group through, has been used to promote compassion and understanding in medical education (Salas et al.,

2013), healing and reconciliation for post-war communities (Dirnstorfer & Saud, 2020), mental-health recovery

(Moran & Alon, 2011), and healing for adolescents in refugee camps (Edelbi, 2020). It aims to create a deeper,

multi-dimensional understanding of experiences — building empowerment and engagement in decision-making.

These approaches interrupt the theatrical experience and charge participants with decision making, analysis, and

sharing judgment, blurring the lines between actors and spectators. The audience may have strong visceral and

emotional reactions to a piece, but they are more likely to take social action when they are invited to act in the

experience of the theatre. Functionally, these approaches work in support of critical thinking and participatory

engagement via what Brecht refers to as a “dialectical” approach, focused on meaning making through

engagement of potentially opposing perspectives (Mumford, 2008).

These forms of theatre align with our goal to create participatory spaces that bring people together to learn how

to speak and embody data, and leverage this knowledge towards goals within their communities. The element of

shared discovery and storytelling, structurally influenced by the perspectives and experiences of participants, can

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create a sense of control and responsibility amongst the collaborators (PARCOS, 2020). In the words of social

science dramatists and researchers Ross Gray and Christina Sindig, “A drama that emerges from such a process

is very much a negotiated settlement, a collective achievement” (Gray & Sinding, 2002). Participant audiences

reconcile with their experiences through story building, a process that connects strongly to the definition of

critical data literacy.

2.3. Professional performances

Finally, we looked to professional theatre practice for inspirations of performative data productions in action.

The 2015 piece A Sort of Joy (Thousands of Exhausted Things), created by the theatre troupe Elevator Repair

Service and the data visualization group Office of Creative Research (Thorp, 2015) uses metadata from the New

York City Museum of Modern Art’s collection to create a performance that highlights the gender of artists whose

pieces are in the collection. Performed at the MoMA itself, the piece centers around faux visitors reading out

names in cadence. A circle of men stands with three women apart. The men speak other male names for several

minutes, until finally the women speak for the first time - saying “Mary” to highlight the gender gap in museum

acquisitions. The entire structure of the piece pulls the audience into the act of questioning the gender diversity

of the MoMA’s collection though experiencing the gaps in the representation via their eyes, ears, and body as it

awaits the next time the women speak.

To further explore and understand professional practice, we conducted two interviews with professionals in the

field of theatre. Our first interview was with Christopher Ellinger, Founder and Director of True Story Theater, a

nonprofit playback theatre company that offers improvisational performances and workshops to community

groups, businesses, and individuals. Ellinger shared a wealth of experiences around how to help participants

develop self-awareness and promote empowerment, reconciliation, and decision-making. Our second interview

was with Frederica Fragapane, an award-winning Italian information designer, who researches the mutualistic

relationship between data visualization and performance. Some of her work has integrated traditional practices

with projected data visualizations that reflect what is happening in the play - such as network diagrams of

character interactions projected behind an unfolding scene (Fragapane, 2017). Her work treats theatrical

production as an artifact for interpretation and representation via standard data visualization approaches; an

alternate conception of what data theatre could be.

3. Workshop and activity design

With this theoretical grounding, and related work across disciplines, we used an iterative process to develop a set

of data theatre activities centered on justice. Over three workshops we iterated on the design of the activities with

students and qualitatively assessed the experiences against our research questions and motivations. Each

workshop focused on one of our driving research questions, while touching on all of them. Our workshops are

also informed by interviews with two professionals in the field of participatory theatre. In this section we

summarize our methods, workshops, and observations.

3.1. Workshop structure and development

The three workshops included (1) an initial exploratory workshop focused on RQ1- Literacy, (2) a prototype

theatre-focused workshop focused on RQ2 - Critique, and (3) a single session in-class workshop focused on RQ3

- Impact (Table 1).

Table 1. Workshop format and details

Workshop #1

Workshop #2

Workshop #3

Exploring Movement

May 2019

Prototype Data Theatre

December 2020

In-Class Data Theatre

February 2021

In-person

Graduate students

Virtual (via Zoom)

Theatre undergraduates

In-person

Theatre undergraduates

15 participants

Self-selected

Focused on RQ1 - Literacy

10 participants

Self-selected

Focused on RQ1 - Critique

12 participants

Class members

Focused on RQ3 - Impact

Each workshop was built around an introductory experience to set tone and activate participants’ bodies,

followed by two in-depth activities. Workshops were 1.5 hours, with interspersed reflection discussions between

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activities. Discussions with participants during the sessions were facilitated as semi-structured focus groups,

consisting of thematic prompts from the facilitator followed by open time for reaction and engagement.

At each workshop we utilized the same data handouts. These included

• A 1-page handout with two charts about ice cream consumption in the US: a line chart of per-capita

consumption by year, and results of a small survey about favorite flavors.

• A 4-page handout about issues of food security in a local community, based on surveys administered by a

non-profit. This included items such as tables summarizing time spent getting groceries, quotes about

barriers to acquiring appropriate food, and bar charts of population demographics.

• A 4-page handout about healthy eating habits from a government-run local early childhood support program.

This included tables of population demographics, bar charts indicating family size and country of origin,

quotes about preferred patterns of eating, and more.

Between sessions we iterated on the activities and incorporated virtual or physical constraints in addition to

participant feedback. In Workshops #1 and #2 we attempted to engage participants as co-designers of the

experience; commenting on, and suggesting ideas for, how to run activities in this domain. This deliberately

reinforced our approach to justice and empowerment, not just in terms of product but also the processes we

undertook. Due to the speculative nature of this work, and our desire to have participant feedback shape next

steps, we hoped this format would allow space for topics and reflections we did not anticipate to emerge.

3.2. Data collection and analysis

Multiple streams of data were collected at each workshop. Each workshop was video recorded and automatically

transcribed to support later evaluation work. During the workshops, members of the research team took on one of

three roles:

• Facilitator: the leader responsible for introducing activities, monitoring progress during breakout sessions,

and prompting reflection.

• Non-participant observer: a “fly on the wall” tasked with taking notes about the interactions and reactions of

participants during the session itself, including notable participant statements and reflections.

• Participant: worked alongside students and contributed comments on the social dynamics of the session after

the fact.

We held a short debrief after each workshop to share comments and reflections, with collective note-taking

happening in a shared online document.

We conducted evaluation through qualitative review of these outputs. One team member reviewed the video

transcription, observer notes, and debrief notes to synthesize high level findings. This involved looking for

recurring observations based on connections to our three research questions and extracting key quotes from

participants that were marked or mentioned by researchers in any of the notes taken. We took a hybrid approach

to doing this qualitative descriptive coding, based on both our primary research questions and topics that

emerged within the transcriptions and notes themselves. We deemed this appropriate because while we do have

some key research questions, this work was also highly exploratory in nature. Findings from this qualitative data

analysis were brought to the larger research team for reflection and further discussion.

3.3. Workshop #1: Exploring movement and data literacy

Our first workshop was an exploration of movement and data designed to surface initial reactions and reflections

(Table 1). The driving research question for this workshop was RQ1 - Literacy. We structured it to evaluate what

kind of basic data literacy skills could be explored via movement and embodiment.

3.3.1. Activities

The two central activities were (1) a “puppeting” experience and (2) a collaborative design of a short

performance in small teams.

Activity #1 – Puppeting: This activity was inspired by image theatre (see Section 2.2), where participants

“sculpt” bodies to embody perspectives, and augmented by movement cards (Figure 1) we developed through a

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preliminary review of movement research. In this activity, each group of four received a movement card (Figure

1) and a dataset (Figure 2). Each pair was invited to review the data handout, about ice cream consumption in the

US (Figure 3), and then to instruct the other pair on positioning their bodies to express the data, constraining

their actions to the movement prompt card they received. Then the roles switched.

Figure 1. A sample of the movement cards provided to participants

Figure 2. The ice-cream data handout

Activity #2 – Collaborative design of a short performance: Each group received one of two multi-page

qualitative and quantitative data handouts; one focused on local food security data, the other on a local program

around healthy eating. The groups were instructed to review and discuss the data handout and develop a series of

movements to represent a story that they saw within it. After about 15 minutes of work time, the groups were re-

engaged to share their performances (if they wished to).

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100

Figure 3. Participants reviewing data and planning their performance

3.3.2. Observations and reflections

Some of the pieces created included:

• Puppeting bodies to mimic the curve of a line chart showing ice cream consumption over time.

• Puppeting peers to mime ice cream being scooped and served based on select rates of consumption.

• A performance representing hunger via painful expressions and doubling over, and highlighting barriers to

food access by performing the wait at a bus stop.

The overall workshop was both playful and reflective. We were successfully able to create an atmosphere of just

trying things out; as seen in comments about “we were having fun,” “that was really fun,” and “all that data stuff

was a lot of fun.” The last one is particularly telling for us, because it suggests that the participant did not expect

working with data to be fun.

Specifically related to RQ1 - Literacy, we saw initial evidence that participants were engaging with some core

areas of data literacy: encodings, representation, and editorial choices in storytelling.

The two puppeting examples shared above indicate that participants were exploring a variety of modes for

representing data in physical form. The first group commented that they were recreating the chart “through

bodily movements like a one-to-one translation.” This suggests to us that this team was exploring how to bring

the existing 2D representation into a 3D space with their bodies. Perhaps their meaning-making through

mimicking the chart might be like learning to read a graph? On the other hand, the second example, miming ice

cream, to us was akin to using symbols to represent data, as in pictographs. They were using the literal act

captured in the data, eating ice cream, to represent to the audience a story they saw in the data. This reflects

evidence from other work where we found early novice data literacy learners often draw symbolic depictions to

represent data (Bhargava et al., 2021). Taken together, these two examples show participants engaged in

explorations of body-based representation that brings them into contact with questions asked in more traditional

data literacy introductions. They stated how the intention to “to use the movements to explain the data,” and

“used different movements for different quantities.” The visual encoding space of traditional charts is well-

document and explored, but the encoding and representations possible via embodiment merit further

investigation.

We were also provoked into questions about predisposed notions of “analysis” and “embodiment” being separate

tasks. One participant noted “it’s really weird to think about how to turn numbers into something... having no

scales changes things.” This offers a potentially interesting hypothesis that relates to prior work reviewed on

education and embodiment (Lakoff & Johnson, 1999; Höök, 2018; D’Ignazio & Klein, 2020), specifically that

non-graph-like embodied ways of understanding were being dismissed by participants even when prompted to

create embodied representations of knowledge. Interestingly, one participant went further and pointed out that

the limits of embodiment also mattered - “what are we trying to do… what can we not do physically?” Relatedly,

another commented that it “was really interesting to think about how you make scales different based on the type

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of visualization”; perhaps demonstrating an ability to reflect on chart design decisions after the physical act of

embodying the data.

The performance activity showed participants wrestling with questions of turning a data into a story. One noted

that “it is really hard to represent all of the data,” a reflection related to questions of what data is included in a

story. When prompted, one participant more directly commented that “I found the hardest part was to create a bit

of a narrative there… how do we take the different parts of the data and make a story out of it.” One participant

in response commented that “even with the same graph, we interpret” - perhaps pointing to an early

understanding that any data can contain a multitude of stories. Another pointed out that as an audience member

you “don’t have a context for the data… you’re almost looking more closely, trying to process every movement.”

Storytelling with data is a difficult skill, but these comments suggest that the performance activity in particular

was leading participants to wrestle with it. This provides early evidence that our two activities were helping

participants explore some fundamental pieces of data literacy - specifically how to represent data and turn it into

a story.

On a final note, we found most people focused on acting out stories from the data as skits, while a few translated

the data into more abstract dance-like movement. Their skits were very narrative in their structure, while the

dance seemed to be evoking emotions they saw in the data. This drove a shift in our project structure, offering

two paths: (1) diving into data and dance (publication forthcoming), and (2) exploring data and theatre (described

in this paper).

3.4. Workshop #2: Prototype data theatre

We specifically designed the next workshop to use practices of participatory, socially engaged theatre to build

data literacy in a group of participants that did not see themselves as “speaking data.” We built on a Brechtian

approach to theatrical practice and invited participants in as co-designers of the activities; after each activity we

asked them to take off their “participant hat” and comment on the design of the activity itself with their

“facilitator/educator hat.” Building on what we observed at the first exploratory workshop, and our learnings

from the field of participatory theatre, our driving research question for this workshop was RQ2 - Critique.

3.4.1. Activities

The two central activities were (1) a collective “puppeting” experience and (2) creating short data skits in small

groups.

Activity #1 – Collective Puppeting: Here we extended the puppeting activity to the group at large - pairs of

participants designed ways to showcase something they saw in the ice-cream data by instructing the rest of us

about what to do with our bodies. This connected to our goal of breaking down the divide between audience and

performers - inviting the entire audience to make meaning by performing a piece. The reflection discussion

afterward focused on questions more targeted at critical thinking.

Activity #2 – Data Skits: After discussing what makes up a “story” (versus just raw data) we introduced the idea

of finding a story and designing a 2–3-minute skit that tells that story. To dig into RQ3 - Impact a bit more we

asked them to focus on conveying one key emotion to their audience. They were given about 15 minutes for this

activity and invited to perform their piece after that.

3.4.2. Observations and reflections

Some of the pieces created included:

• Puppeting the group to compose a line chart with their arms across a grid of video boxes in Zoom.

• Puppeting one participant at a time to represent the popularity of a flavor of ice cream by trying to represent

it via movement and sound.

• A skit comparing time spent getting to a grocery store by different populations via pretending to drive a car.

• A skit acting out a debate between two fictional community members about whether the data about cultural

eating norms was reductive, stereotypical and offensive.

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Overall, we were pleasantly surprised at the engagement from participants despite the constraints of the virtual

setting. We hypothesize that leading silly introductory activities within the boxes on Zoom created a sense of

failure and awkwardness being a normal part of the process, freeing participants to be slightly more uninhibited

in their ideas. While reflecting on this first activity, some participants shared that they were a little lost at first,

another commented that “once we got into it, it was really really fun.” Again, this reinforced a finding from

Workshop #1, specifically that embodying data via our activities was a fun process.

The car driving skit provided an evocative example of using theatrics to create space for questioning in the

audience (similar to the A Sort of Joy piece described in 2.3). The awkward silence of the final driver continuing

to drive for far too long created space to question why their drive was so much longer.

A number of comments offered early evidence that we are creating a new pathway for learners who perceive

themselves as “non-numbers” people. An incidental conversation as the workshop was starting led to many

participants self-identifying as “non-numbers people.” This binary identity formation in relation to math creates a

real barrier to entering domains that learners assess as numbers-heavy (Leonard et al., 2020). In future

workshops we would more robustly capture this in a pre-survey, providing an informal qualitative indication of

how the group felt. In addition, we could embrace Leonard’s notion of “naming discomfort” as a potential

intentional response to try break down any perceived barriers felt by participants.

The debate skit mentioned above provided our strongest example of participants engaging in critical questioning

of datasets and their use (as described in RQ2 – Critique). They challenged the assumptions they saw in the data.

Based on our decade of experience running data literacy activities we found this quote notable - critiquing the

data like this rarely happens with novices. The fact that this group did just that suggests that we were able to

create an environment where they felt comfortable engaging their critical thinking skills despite the power

dynamics at play within the workshop. In addition, some of this early evidence related to our commentary on

control of impact (section 1.1). Our choice to use local data resonated with a number of the participants. One

noted that our approach “allows communities to redefine what is a hard-tangible fact with emotions” and lets

“the audience have freedom to take from it what they are going to take from it.” These examples suggest that our

Freire-inspired approach to designing with community data with impact might have successfully introduced the

basics of the critical data literacy concept introduced previously.

At the process level, one helpful comment suggested that calling them data “skits” was inappropriate; the term

implies humor, and this was not engaging in humorous approaches.

3.5. Workshop #3: In-class data theatre

Our third workshop was a single session in one of the author’s in-person Movement course for undergraduate

theatre majors. As with workshop #2, our main goals revolved around testing the activities to determine how

they support helping a new group of participants start to move from data to story through the design of

participatory performances.

3.5.1. Activities

The two central activities were (1) a prompt to “embody a dataset” and (2) a prompt to “make a (data) scene.”

Activity #1 – Embody a Dataset: We did not substantially alter this activity.

Activity #2 – Make a (Data) Scene: Building on our learning from Ellinger of True Story Theatre (see 2.3), we

added a bit more intentionality in how we introduced the data. Using the same handouts, we took more time to

address the fact that they might connect to negative experiences of people in the room and we attempted to create

space to honor and digest that. Additionally, we began using the term “scene” in place of “skit.”

3.5.2. Observations and reflections

Some of the pieces created included:

• Embodying data about ice cream consumption over time by directing the class to move around the space and

hum, while varying volume and speed based on the data instructions.

• Embodying data about consumption levels via standing at different heights.

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• A scene making physical all the obstacles that a disadvantaged person faces in accessing food for her family,

from time, to distance, to money, in a journey to feed her children.

• A dramatic scene where a character was taunted with healthy food and other resources that were too high

above her head or too fast for her to catch.

The “embody” activity offered two interesting notes. The first related to sound and movement - this was the first

intentional use of encoding via sound that we saw. We found it encouraging that these activities led participants

to a mode of data representation that has only recently gained in popularity - sonification (Lenzi & Ciuccarelli,

2020). The second relates to “binning” - the process in statistics of grouping a set of discrete data into larger

categories. In this specific case, the team quickly averaged ice cream consumption levels by decade to instruct

participants how to stand. In more traditional data literacy programs, we often see learners introduced to the idea

of binning and how it can alter the results of an analysis (based on bin size). Here we saw the participants come

to the idea themselves and used the opportunity to discuss the idea and concerns during the reflection after they

presented it. These both suggest interesting learnings related to the analysis and representation pieces of data

literacy.

Regarding RQ3 - Impact, we saw early indications of participants reflecting on the subjects of the data in

meaningful ways. One group noted that the “make a scene” activity helped them get around the dehumanization

that can occur in traditional representations, saying specifically that “numbers / statistics are not just numbers,

they represent people going through this.” A viewer of that scene said it “pulled me in because it matched

something that happened to me in my life.” These comments help us dig into some of the driving questions

related to how performing a dataset could change perspectives on those represented by the data, and potentially

build empathy for their situation. Furthermore, one participant started to get at a potentially causal link that

merits more investigation, stating that their “emotional response to the data was a lot stronger when we

embodied the data.” However, another commented that one weakness is how “the paper visual representation is

stronger because you can reflect back and look at it … it is locked in time.” These suggest the participants were

engaged in the act of embodying the data and were reflecting on its affordances; there may be aspects such as

recall that are not supported through embodied representation.

3.6. Synthesizing two activities

Based on our interaction and reflections from the 3 separate workshops we synthesized our learnings into two

data theater learning activities for novice audiences. We offer these as the foundation of an approach to data

theatre in service of equity and engagement in learning settings that focus on justice. The “Embody a Dataset”

and “Make a (Data) Scene” activities are documented in activity guides for educators attached as supplemental

material to this paper.

4. Synthesis and discussion

In this section we highlight findings and reflections across workshops and research questions. In particular, we

synthesize and discuss our initial findings in relation to each of our guiding research questions. With our focus

on undergraduate theatre students as primary participants, findings may not be generalizable to other audiences

we plan to work with, such as community organizations. However, the findings do suggest an initial foundation

on which to build and are applicable for others wishing to work with this same or related audiences. Our early

observations suggest that data theatre is a novel entry point into data literacy, worthy of more study (as detailed

in 5 below).

4.1. Creating many paths for many learners (RQ1 - Literacy)

Our initial workshops and prototype activities suggest that data theatre can introduce participants to several core

data literacy skills - reading data, picking representations, and creating stories.

Our activities were appealing to audiences that might not be drawn to spreadsheets, math, and the computational

analytic tools typically used in introductory data literacy sessions. Participants associated the term “data” with

other words such as “information,” “numbers,” “research,” “charts,” and “technology.” When prompted to

represent “data” with a sound of movement, we heard computational bleeps, bloops, and mechanized motions. In

addition, we found in Workshops #2 and #3 that our participants did not identify as “math people” (Leonard et

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al., 2020). One participant noted, “You are translating your math mind into a creative mind, into a story mind,

into a people mind.” Another participant echoed that “(we were) looking at a piece of data and the numbers and

quantitative stuff, but what’s the story here.” A third said that “I think that is part of finding the stories, you have

to translate.” These comments suggest that participants were building basic data literacy skills, specifically in the

realms of representation and storytelling. Our responses could employ Cribbs’ proposed pathway from

“performance” to “recognition” to “mathematics identify” more intentionally in response to these types of

comments about engaging different parts of one’s brain (Cribbs et al., 2015). Combined, these observations

deepen our motivation to create pathways into data literacy for learners who are not attracted to the existing

computation-centered introductions, specifically related to our concern about citizens’ abilities to join in civic

discussions that are increasingly data-centered.

This returns to our goal of validating multiple ways of knowing in the field of data science. Specifically informed

by the concept of epistemological pluralism (Turkle & Papert, 2015) and data feminism (D’Ignazio & Klein,

2020), data theatre can serve as an entry point for learners that take a different approach to thinking through data.

For instance, while many introductions to data begin with aggregating a spreadsheet, we saw many of our

participants begin by trying to understand one data point. Perhaps like the story of Anne that Turkle and Papert’s

share, where the “bricoleur programmer” covers her bird like a painter might, data science needs to embrace

solutions that are informed by approaches from other domains, even if they seem “sub-optimal” or “inefficient”

to experts. Our “data point explorer” should also be welcomed as having a valid way of exploring a dataset.

Another connection here is to the idea of kinesthetic memory as another way of knowing (Lowenfield, 1957).

Participants in our workshops were using their body to process, understand, and express data concepts and

interpretations.

4.2. Building critical data literacy (RQ2 - Critique)

In this context, we see data theatre as a promising approach that could help engage a critical frame of mind about

the data itself, though this work did not engage critique of data’s use directly.

It is illuminating to bring this discussion back to Tygel and Kirsch’s (2015) conceptualization of critical data

literacy. Our participants pointed out that “statistics and data sometimes generalize experiences and are not

telling the whole story just by looking at the numbers,” and “using reflection and performance shows the story

underneath it that the data attempts to bury.” These comments suggest that, as Tygel and Kirsch (2015) define it,

our participants are seeing data as an “output of a social process.” In our second workshop, one participant noted

that “I was really self-conscious about being critical of the data set,” but then they proceeded to despite that

hesitation (creating the data debate described in section 3.4.2). In reference to Tygel and Kirsch’s (2015)

definition again, we argue this group demonstrated data “manipulated based on explicit objectives” and including

a “social evaluation of what message should be transmitted.” Specifically, we found participants were willing to

interrogate and question data when asked to physically embody it. As one participant noted in the Workshop #3,

you “bring your own interpretation or experience to the performance.” A data feminist might suggest you do the

same with data - “consider context” is the sixth principle of data feminism (D’Ignazio & Klein, 2020).

We saw less evidence that we effectively prompted critical reflection about the use of data in social settings as

disempowering or emancipatory. This has become more important to engage as large companies with massive

technical capacity continue to use data-based systems to impact far larger swaths of the population, with more

significant impacts on the historically marginalized (Noble, 2018). If we create new paths to engage populations

in civic data-driven decision making, our definition of justice indicates it must be in service of those with the

most potential to be harmed by the decision. Our prompts were not designed to help participants consider the

impacts of data use in a community.

4.3. Creating impactful embodied experiences (RQ3 - Impact)

These initial workshops lead us to believe that our “Make a (Data) Scene” activity can rehumanize data and

potentially build empathy and engagement for participants, but we are left with more questions about impacts for

the audience.

One consistent reflection in our work was that the theatrical embodiments of these data points help participants

rehumanize the data. Typical introductions to data are decontextualized from the subjects of the data, and their

points of impact (Oliver & McNeil, 2021), a critique we have made in other work about introductions to data

literacy (D’Ignazio & Bhargava, 2015). We argue that with a justice-focused lens it is centrally important to

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integrate the lived experience of those represented within a dataset, and the context of its potential implications.

Participants reflected that the scene activity “grounded it in the actual people” and “got me thinking about the

people behind the data, behind the numbers.” They were “telling the story of the people behind the data.” This

provides early evidence that embodying data may have helped these learners remember that data often represents

people and experiences. This work allows them to consider their own experiences and knowledge in relation to

the communities the data is about — key steps for building empathy.

Furthermore, we heard some reflections suggesting that our activities created impactful reflective experiences for

the participants themselves. For instance, responding to a question about the datasets, one participant noted that it

“pulled me in because it matched something that happened to me in my life.” This comment reinforces findings

from the field of teaching “data for good,” where educators have found that learners get drawn in by working on

datasets about problems they have experienced or are interested in (Bhargava, in press). This also relates to

comments from our interview with Ellinger of True Story Theatre, about creating space to process seeing oneself

in the data — engaging the audience in empathetic reasoning.

5. Conclusions and next steps

In this paper we discuss the motivations, inspirations, process, and findings from the creation of a set of “data

theatre” activities in a higher education setting. We offer the activities as an alternate entryway to building a

critical data literacy, one that builds on processes rooted in questions of justice and equity, decentering

technology and inviting sets of learners who are not engaged through current approaches. We find early evidence

that the data theatre approach is effective at helping participants build some data literacy skills without

introductions to statistics and computational tools, reflect critically on datasets and their intended use, and

engage in emotionally meaningful embodied performance of data stories.

In a datafied world, we argue that democratic societies are morally responsible to govern through understandable

mechanisms. Data theatre offers one path to making data more understandable, particularly for learners without

access to technology, or who do not take to spreadsheets. We hope the activity guides included as appendices

contribute concretely to other educators’ toolboxes. Theatre based introductions to data literacy can be an

effective entry point into data literacy that complements other approaches and potentially lowers barriers to

certain content based on their embodied nature.

5.1. Next steps

In an educational setting and a community setting, these introductions should serve as tools of empowerment for

populations typically left out of data-centered decision processes. We hope to move beyond the college

classroom in future work, trying these activities out with community groups that work on issues of justice that

involve data. The coronavirus pandemic limited our ability to start with these groups, and in-person workshop

with them would remove the barriers of access to bandwidth and computers for online Zoom sessions.

These initial observations and examples present us with numerous potential next steps focused on exploration,

formalization, and robustness. For instance, we could administer pre- and post-surveys at workshops asking

participants about their confidence levels vis-a-vis working with data. This would ascertain whether a single

experience with these activities creates a short-term impact on self-assessed abilities, and perhaps build an

evidence base for overcoming skills-based identity barriers to working with data. Similarly, to explore the

challenge of building empathy with subjects of data, we could more directly ask learners how connected they

feel to the data and the performance they created.

Particularly related to creating impact on the audience, we believe there is more theoretical grounding from

theatrical practice to explore. One path forward would connect more directly to the concept of “kinesthetic

empathy” - the idea that audiences experience empathy by watching a performance (Martin, 1975; Reynolds &

Reason, 2012). Another relates to further exploration of feminist theatre practice and the idea of creating

spectacle via performance in public place. The work of the Women’s Street Theatre Group at the 1970 Miss

World competition is an evocative example (Cowley et al., 1971). In addition, we could engage more directly

with the Brechtian “dialectical theatre” approach of interrupting performance to engage the audience. A

participant noted we could “think about not only how it can abstract from a situation but also how you can re-

invite into that situation.”

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